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  • richardmitnick 7:23 am on July 22, 2019 Permalink | Reply
    Tags: "Making it easier to program and protect the web", Associate Professor Adam Chlipala, “I hope to save people a lot of time doing repetitive work by automating programming work as well as decreasing the cost of building secure reliable systems” says Associate Professor Adam Chlipala, MIT, Much of his work centers on designing simplified programming languages and app-making tools for programmers; systems that automatically generate optimized algorithms for specific tasks; and compilers   

    From MIT News: “Making it easier to program and protect the web” 

    MIT News

    From MIT News

    July 20, 2019
    Rob Matheson

    1
    “I hope to save a lot of people a lot of time doing boring, repetitive work by automating programming work as well as decreasing the cost of building secure, reliable systems,” says Associate Professor Adam Chlipala. Image: M. Scott Brauer

    Professor Adam Chlipala builds tools to help programmers more quickly generate optimized, secure code.

    Behind the scenes of every web service, from a secure web browser to an entertaining app, is a programmer’s code, carefully written to ensure everything runs quickly, smoothly, and securely. For years, MIT Associate Professor Adam Chlipala has been toiling away behind behind-the-scenes, developing tools to help programmers more quickly and easily generate their code — and prove it does what it’s supposed to do.

    Scanning the many publications on Chlipala’s webpage, you’ll find some commonly repeated keywords, such as “easy,” “automated,” and “proof.” Much of his work centers on designing simplified programming languages and app-making tools for programmers, systems that automatically generate optimized algorithms for specific tasks, and compilers that automatically prove that the complex math written in code is correct.

    “I hope to save a lot of people a lot of time doing boring repetitive work, by automating programming work as well as decreasing the cost of building secure, reliable systems,” says Chlipala, who is a recently tenured professor of computer science, a researcher in the Computer Science and Artificial Laboratory (CSAIL), and head of the Programming Languages and Verification Group.

    One of Chlipala’s recent systems automatically generates optimized — and mathematically proven — cryptographic algorithms, freeing programmers from hours upon hours of manually writing and verifying code by hand. And that system is now behind nearly all secure Google Chrome communications.

    But Chlipala’s code-generating and mathematical proof systems can be used for a wide range of applications, from protecting financial transactions against fraud to ensuring autonomous vehicles operate safely. The aim, he says, is catching coding errors before they lead to real-world consequences.

    “Today, we just assume that there’s going to be a constant flow of serious security problems in all major operating systems. But using formal mathematical methods, we should be able to automatically guarantee there will be far fewer surprises of that kind,” he says. “With a fixed engineering budget, we can suddenly do a lot more, without causing embarrassing or life-threatening disasters.”

    A heart for system infrastructure

    As he was growing up in the Lehigh Valley region of Pennsylvania, programming became “an important part of my self-identity,” Chlipala says. In the late 1980s, when Chlipala was young, his father, a researcher who ran physics experiments for AT&T Bell Laboratories, taught him some basic programming skills. He quickly became hooked.

    In the late 1990s, when the family finally connected to the internet, Chlipala had access to various developer resources that helped him delve “into more serious stuff,” meaning designing larger, more complex programs. He worked on compilers — programs that translate programming language into machine-readable code — and web applications, “when apps were an avant-garde subject.”

    In fact, apps were then called “CGI scripts.” CGI is an acronym for Common Gateway Interface, which is a protocol that enables a program (or “script”) to talk to a server. In high school, Chlipala and some friends designed CGI scripts that connected them in an online forum for young programmers. “It was a means for us to start building our own system infrastructure,” he says.

    And as an avid computer gamer, the logical thing for a teenaged Chlipala to do was design his own games. His first attempts were text-based adventures coded in the BASIC programming language. Later, in the C programming language, he designed a “Street Fighter”-like game, called Brimstone, and some simulated combat tabletop games.

    It was exciting stuff for a high schooler. “But my heart was always in systems infrastructure, like code compilers and building help tools for old Windows operating systems,” Chlipala says.

    From then on, Chlipala worked far in the background of web services, building the programming foundations for developers. “I’m several levels of abstraction removed from the type of computer programming that’s of any interest to any end-user,” he says, laughing.

    Impact in the real world

    After high school, in 2000, Chlipala enrolled at Carnegie Mellon University, where he majored in computer science and got involved in a programming language compiler research group. In 2007, he earned his PhD in computer science from University of California at Berkeley, where his work focused on developing methods that can prove the mathematical correctness of algorithms.

    After completing a postdoc at Harvard University, Chlipala came to MIT in 2011 to begin his teaching career. What drew Chlipala to MIT, in part, was an opportunity “to plug in a gap, where no one was doing my kind of proofs of computer systems’ correctness,” he says. “I enjoyed building that subject here from the ground up.”

    Testing the source code that powers web services and computer systems today is computationally intensive. It mostly relies on running the code through tons of simulations, and correcting any caught bugs, until the code produces a desired output. But it’s nearly impossible to run the code through every possible scenario to prove it’s completely without error.

    Chlipala’s research group instead focuses on eliminating the need for those simulations, by designing proven mathematical theorems that capture exactly how a given web service or computer system is supposed to behave. From that, they build algorithms that check if the source code operates according to that theorem, meaning it performs exactly how it’s supposed to, mostly during code compiling.

    Even though such methods can be applied to any application, Chlipala likes to run his research group like a startup, encouraging students to target specific, practical applications for their research projects. In fact, two of his former students recently joined startups doing work connected to their thesis research.

    One student is working on developing a platform that lets people rapidly design, fabricate, and test their own computer chips. Another is designing mathematical proven systems to ensure the source code powering driverless car systems doesn’t contain errors that’ll lead to mistakes on the road. “In driverless cars, a bug could literally cause a crash, not just the ‘blue-screen death’ type of a crash,” Chlipala says.

    Now on sabbatical from this summer until the end of the year, Chlipala is splitting his time between MIT research projects and launching his own startup based around tools that help people without programming experience create advanced apps. One such tool, which lets nonexperts build scheduling apps, has already found users among faculty and staff in his own department. About the new company, he says: “I’ve been into entrepreneurship over the last few years. But now that I have tenure, it’s a good time to get started.”

    See the full article here .


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  • richardmitnick 9:35 am on July 16, 2019 Permalink | Reply
    Tags: "Automated system generates robotic parts for novel tasks", , , MIT, Navigating the “combinatorial explosion”, The researchers adopted a computer graphics technique called “ray-tracing” which simulates the path of light interacting with objects.   

    From MIT News: “Automated system generates robotic parts for novel tasks” 

    MIT News

    From MIT News

    July 12, 2019
    Rob Matheson

    1
    A new MIT-invented system automatically designs and 3-D prints complex robotic actuators optimized according to an enormous number of specifications, such as appearance and flexibility. To demonstrate the system, the researchers fabricated floating water lilies with petals equipped with arrays of actuators and hinges that fold up in response to magnetic fields run through conductive fluids. Credit: Subramanian Sundaram

    When designing actuators involves too many variables for humans to test by hand, this system can step in.

    An automated system developed by MIT researchers designs and 3-D prints complex robotic parts called actuators that are optimized according to an enormous number of specifications. In short, the system does automatically what is virtually impossible for humans to do by hand.

    In a paper published today in Science Advances, the researchers demonstrate the system by fabricating actuators — devices that mechanically control robotic systems in response to electrical signals — that show different black-and-white images at different angles. One actuator, for instance, portrays a Vincent van Gogh portrait when laid flat. Tilted an angle when it’s activated, however, it portrays the famous Edvard Munch painting “The Scream.” The researchers also 3-D printed floating water lilies with petals equipped with arrays of actuators and hinges that fold up in response to magnetic fields run through conductive fluids.

    The actuators are made from a patchwork of three different materials, each with a different light or dark color and a property — such as flexibility and magnetization — that controls the actuator’s angle in response to a control signal. Software first breaks down the actuator design into millions of three-dimensional pixels, or “voxels,” that can each be filled with any of the materials. Then, it runs millions of simulations, filling different voxels with different materials. Eventually, it lands on the optimal placement of each material in each voxel to generate two different images at two different angles. A custom 3-D printer then fabricates the actuator by dropping the right material into the right voxel, layer by layer.

    “Our ultimate goal is to automatically find an optimal design for any problem, and then use the output of our optimized design to fabricate it,” says first author Subramanian Sundaram PhD ’18, a former graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We go from selecting the printing materials, to finding the optimal design, to fabricating the final product in almost a completely automated way.”

    The shifting images demonstrates what the system can do. But actuators optimized for appearance and function could also be used for biomimicry in robotics. For instance, other researchers are designing underwater robotic skins with actuator arrays meant to mimic denticles on shark skin. Denticles collectively deform to decrease drag for faster, quieter swimming. “You can imagine underwater robots having whole arrays of actuators coating the surface of their skins, which can be optimized for drag and turning efficiently, and so on,” Sundaram says.

    Joining Sundaram on the paper are: Melina Skouras, a former MIT postdoc; David S. Kim, a former researcher in the Computational Fabrication Group; Louise van den Heuvel ’14, SM ’16; and Wojciech Matusik, an MIT associate professor in electrical engineering and computer science and head of the Computational Fabrication Group.

    Navigating the “combinatorial explosion”

    Robotic actuators today are becoming increasingly complex. Depending on the application, they must be optimized for weight, efficiency, appearance, flexibility, power consumption, and various other functions and performance metrics. Generally, experts manually calculate all those parameters to find an optimal design.

    Adding to that complexity, new 3-D-printing techniques can now use multiple materials to create one product. That means the design’s dimensionality becomes incredibly high. “What you’re left with is what’s called a ‘combinatorial explosion,’ where you essentially have so many combinations of materials and properties that you don’t have a chance to evaluate every combination to create an optimal structure,” Sundaram says.

    In their work, the researchers first customized three polymer materials with specific properties they needed to build their actuators: color, magnetization, and rigidity. In the end, they produced a near-transparent rigid material, an opaque flexible material used as a hinge, and a brown nanoparticle material that responds to a magnetic signal. They plugged all that characterization data into a property library.

    The system takes as input grayscale image examples — such as the flat actuator that displays the Van Gogh portrait but tilts at an exact angle to show “The Scream.” It basically executes a complex form of trial and error that’s somewhat like rearranging a Rubik’s Cube, but in this case around 5.5 million voxels are iteratively reconfigured to match an image and meet a measured angle.

    2
    Initially, the system draws from the property library to randomly assign different materials to different voxels. Then, it runs a simulation to see if that arrangement portrays the two target images, straight on and at an angle. If not, it gets an error signal. That signal lets it know which voxels are on the mark and which should be changed. Adding, removing, and shifting around brown magnetic voxels, for instance, will change the actuator’s angle when a magnetic field is applied. But, the system also has to consider how aligning those brown voxels will affect the image.

    Voxel by voxel

    To compute the actuator’s appearances at each iteration, the researchers adopted a computer graphics technique called “ray-tracing,” which simulates the path of light interacting with objects. Simulated light beams shoot through the actuator at each column of voxels. Actuators can be fabricated with more than 100 voxel layers. Columns can contain more than 100 voxels, with different sequences of the materials that radiate a different shade of gray when flat or at an angle.

    When the actuator is flat, for instance, the light beam may shine down on a column containing many brown voxels, producing a dark tone. But when the actuator tilts, the beam will shine on misaligned voxels. Brown voxels may shift away from the beam, while more clear voxels may shift into the beam, producing a lighter tone. The system uses that technique to align dark and light voxel columns where they need to be in the flat and angled image. After 100 million or more iterations, and anywhere from a few to dozens of hours, the system will find an arrangement that fits the target images.

    “We’re comparing what that [voxel column] looks like when it’s flat or when it’s titled, to match the target images,” Sundaram says. “If not, you can swap, say, a clear voxel with a brown one. If that’s an improvement, we keep this new suggestion and make other changes over and over again.”

    To fabricate the actuators, the researchers built a custom 3-D printer that uses a technique called “drop-on-demand.” Tubs of the three materials are connected to print heads with hundreds of nozzles that can be individually controlled. The printer fires a 30-micron-sized droplet of the designated material into its respective voxel location. Once the droplet lands on the substrate, it’s solidified. In that way, the printer builds an object, layer by layer.

    The work could be used as a stepping stone for designing larger structures, such as airplane wings, Sundaram says. Researchers, for instance, have similarly started breaking down airplane wings into smaller voxel-like blocks to optimize their designs for weight and lift, and other metrics. “We’re not yet able to print wings or anything on that scale, or with those materials. But I think this is a first step toward that goal,” Sundaram says.

    See the full article here .


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    Please help promote STEM in your local schools.


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  • richardmitnick 11:56 am on July 12, 2019 Permalink | Reply
    Tags: "Enriching solid-state batteries", , , Jennifer Rupp, , , MIT, , ,   

    From MIT News: Women in STEM-“Enriching solid-state batteries” Jennifer Rupp 

    MIT News

    From MIT News

    July 11, 2019
    Denis Paiste | Materials Research Laboratory

    1
    MIT Associate Professor Jennifer Rupp stands in front of a pulsed laser deposition chamber, in which her team developed a new lithium garnet electrolyte material with the fastest reported ionic conductivity of its type. The technique produces a thin film about 330 nanometers thick. “Having the lithium electrolyte as a solid-state very fast conductor allows you to dream out loud of anything else you can do with fast lithium motion,” Rupp says. Photo: Denis Paiste/Materials Research Laboratory

    Researchers at MIT have come up with a new pulsed laser deposition technique to make thinner lithium electrolytes using less heat, promising faster charging and potentially higher-voltage solid-state lithium ion batteries.

    Key to the new technique for processing the solid-state battery electrolyte is alternating layers of the active electrolyte lithium garnet component (chemical formula, Li6.25Al0.25La3Zr2O12, or LLZO) with layers of lithium nitride (chemical formula Li3N). First, these layers are built up like a wafer cookie using a pulsed laser deposition process at about 300 degrees Celsius (572 degrees Fahrenheit). Then they are heated to 660 C and slowly cooled, a process known as annealing.

    During the annealing process, nearly all of the nitrogen atoms burn off into the atmosphere and the lithium atoms from the original nitride layers fuse into the lithium garnet, forming a single lithium-rich, ceramic thin film. The extra lithium content in the garnet film allows the material to retain the cubic structure needed for positively charged lithium ions (cations) to move quickly through the electrolyte. The findings were reported in a Nature Energy paper published online recently by MIT Associate Professor Jennifer L. M. Rupp and her students Reto Pfenninger, Michal M. Struzik, Inigo Garbayo, and collaborator Evelyn Stilp.

    “The really cool new thing is that we found a way to bring the lithium into the film at deposition by using lithium nitride as an internal lithiation source,” Rupp, the work’s senior author, says. Rupp holds joint MIT appointments in the departments of Materials Science and Engineering and Electrical Engineering and Computer Science.

    “The second trick to the story is that we use lithium nitride, which is close in bandgap to the laser that we use in the deposition, whereby we have a very fast transfer of the material, which is another key factor to not lose lithium to evaporation during a pulsed laser deposition,” Rupp explains.

    Safer technology

    Lithium batteries with commonly used electrolytes made by combining a liquid and a polymer can pose a fire risk when the liquid is exposed to air. Solid-state batteries are desirable because they replace the commonly used liquid polymer electrolytes in consumer lithium batteries with a solid material that is safer. “So we can kick that out, bring something safer in the battery, and decrease the electrolyte component in size by a factor of 100 by going from the polymer to the ceramic system,” Rupp explains.

    Although other methods to produce lithium-rich ceramic materials on larger pellets or tapes, heated using a process called sintering, can yield a dense microstructure that retains a high lithium concentration, they require higher heat and result in bulkier material. The new technique pioneered by Rupp and her students produces a thin film that is about 330 nanometers thick (less than 1.5 hundred-thousandths of an inch). “Having a thin film structure instead of a thick ceramic is attractive for battery electrolyte in general because it allows you to have more volume in the electrodes, where you want to have the active storage capacity. So the holy grail is be thin and be fast,” she says.

    Compared to the classic ceramic coffee mug, which under high magnification shows metal oxide particles with a grain size of tens to hundreds of microns, the lithium (garnet) oxide thin films processed using Rupp’s methods show nanometer scale grain structures that are one-thousandth to one-ten-thousandth the size. That means Rupp can engineer thinner electrolytes for batteries. “There is no need in a solid-state battery to have a large electrolyte,” she says.

    Faster ionic conduction

    Instead, what is needed is an electrolyte with faster conductivity. The unit of measurement for lithium ion conductivity is expressed in Siemens. The new multilayer deposition technique produces a lithium garnet (LLZO) material that shows the fastest ionic conductivity yet for a lithium-based electrolyte compound, about 2.9 x 10-5 Siemens (0.000029 Siemens) per centimeter. This ionic conductivity is competitive with solid-state lithium battery thin film electrolytes based on LIPON (lithium phosphorus oxynitride electrolytes) and adds a new film electrolyte material to the landscape.

    “Having the lithium electrolyte as a solid-state very fast conductor allows you to dream out loud of anything else you can do with fast lithium motion,” Rupp says.

    A battery’s negatively charged electrode stores power. The work points the way toward higher-voltage batteries based on lithium garnet electrolytes, both because its lower processing temperature opens the door to using materials for higher voltage cathodes that would be unstable at higher processing temperatures, and its smaller electrolyte size allows physically larger cathode volume in the same battery size.

    Co-authors Michal Struzik and Reto Pfenninger carried out processing and Raman spectroscopy measurements on the lithium garnet material. These measurements were key to showing the material’s fast conduction at room temperature, as well as understanding the evolution of its different structural phases.

    “One of the main challenges in understanding the development of the crystal structure in LLZO was to develop appropriate methodology. We have proposed a series of experiments to observe development of the crystal structure in the [LLZO] thin film from disordered or ‘amorphous’ phase to fully crystalline, highly conductive phase utilizing Raman spectroscopy upon thermal annealing under controlled atmospheric conditions,” says co-author Struzik, who was a postdoc working at ETH Zurich and MIT with Rupp’s group, and is now a professor at Warsaw University of Technology in Poland. “That allowed us to observe and understand how the crystal phases are developed and, as a consequence, the ionic conductivity improved,” he explains.

    Their work shows that during the annealing process, lithium garnet evolves from the amorphous phase in the initial multilayer processed at 300 C through progressively higher temperatures to a low conducting tetragonal phase in a temperature range from about 585 C to 630 C, and to the desired highly conducting cubic phase after annealing at 660 C. Notably, this temperature of 660 C to achieve the highly conducting phase in the multilayer approach is nearly 400 C lower than the 1,050 C needed to achieve it with prior sintering methods using pellets or tapes.

    “One of the greatest challenges facing the realization of solid-state batteries lies in the ability to fabricate such devices. It is tough to bring the manufacturing costs down to meet commercial targets that are competitive with today’s liquid-electrolyte-based lithium-ion batteries, and one of the main reasons is the need to use high temperatures to process the ceramic solid electrolytes,” says Professor Peter Bruce, the Wolfson Chair of the Department of Materials at Oxford University, who was not involved in this research.

    “This important paper reports a novel and imaginative approach to addressing this problem by reducing the processing temperature of garnet-based solid-state batteries by more than half — that is, by hundreds of degrees,” Bruce adds. “Normally, high temperatures are required to achieve sufficient solid-state diffusion to intermix the constituent atoms of ceramic electrolyte. By interleaving lithium layers in an elegant nanostructure the authors have overcome this barrier.”

    After demonstrating the novel processing and high conductivity of the lithium garnet electrode, the next step will be to test the material in an actual battery to explore how the material reacts with a battery cathode and how stable it is. “There is still a lot to come,” Rupp predicts.

    Understanding aluminum dopant sites

    A small fraction of aluminum is added to the lithium garnet formulation because aluminum is known to stabilize the highly conductive cubic phase in this high-temperature ceramic. The researchers complemented their Raman spectroscopy analysis with another technique, known as negative-ion time-of-flight secondary ion mass spectrometry (TOF-SIMS), which shows that the aluminum retains its position at what were originally the interfaces between the lithium nitride and lithium garnet layers before the heating step expelled the nitrogen and fused the material.

    “When you look at large-scale processing of pellets by sintering, then everywhere where you have a grain boundary, you will find close to it a higher concentration of aluminum. So we see a replica of that in our new processing, but on a smaller scale at the original interfaces,” Rupp says. “These little things are what adds up, also, not only to my excitement in engineering but my excitement as a scientist to understand phase formations, where that goes and what that does,” Rupp says.

    “Negative TOF-SIMS was indeed challenging to measure since it is more common in the field to perform this experiment with focus on positively charged ions,” explains Pfenninger, who worked at ETH Zurich and MIT with Rupp’s group. “However, for the case of the negatively charged nitrogen atoms we could only track it in this peculiar setup. The phase transformations in thin films of LLZO have so far not been investigated in temperature-dependent Raman spectroscopy — another insight towards the understanding thereof.”

    The paper’s other authors are Inigo Garbayo, who is now at CIC EnergiGUNE in Minano, Spain, and Evelyn Stilp, who was then with Empa, Swiss Federal Laboratories for Materials Science and Technology, in Dubendorf, Switzerland.

    Rupp began this research while serving as a professor of electrochemical materials at ETH Zurich (the Swiss Federal Institute of Technology) before she joined the MIT faculty in February 2017. MIT and ETH have jointly filed for two patents on the multi-layer lithium garnet/lithium nitride processing. This new processing method, which allows precise control of lithium concentration in the material, can also be applied to other lithium oxide films such as lithium titanate and lithium cobaltate that are used in battery electrodes. “That is something we invented. That’s new in ceramic processing,” Rupp says.

    “It is a smart idea to use Li3N as a lithium source during preparation of the garnet layers, as lithium loss is a critical issue during thin film preparation otherwise,” comments University Professor Jürgen Janek at Justus Liebig University Giessen in Germany. Janek, who was not involved in this research, adds that “the quality of the data and the analysis is convincing.”

    “This work is an exciting first step in preparing one of the best oxide-based solid electrolytes in an intermediate temperature range,” Janek says. “It will be interesting to see whether the intermediate temperature of about 600 degrees C is sufficient to avoid side reactions with the electrode materials.”

    Oxford Professor Bruce notes the novelty of the approach, adding “I’m not aware of similar nanostructured approaches to reduce diffusion lengths in solid-state synthesis.”

    “Although the paper describes specific application of the approach to the formation of lithium-rich and therefore highly conducting garnet solid electrolytes, the methodology has more general applicability, and therefore significant potential beyond the specific examples provided in the paper,” Bruce says. Commercialization may be needed to be demonstrate this approach at larger scale, he suggests.

    While the immediate impact of this work is likely to be on batteries, Rupp predicts another decade of exciting advances based on applications of her processing techniques to devices for neuromorphic computing, artificial intelligence, and fast gas sensors. “The moment the lithium is in a small solid-state film, you can use the fast motion to trigger other electrochemistry,” she says.

    Several companies have already expressed interest in using the new electrolyte approach. “It’s good for me to work with strong players in the field so they can push out the technology faster than anything I can do,” Rupp says.

    This work was funded by the MIT Lincoln Laboratory, the Thomas Lord Foundation, Competence Center Energy and Mobility, and Swiss Electrics.

    See the full article here .


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  • richardmitnick 12:17 pm on July 10, 2019 Permalink | Reply
    Tags: "Machine learning for everyone", , Attracting students from fields ranging from biology to business to architecture., , MIT   

    From MIT News: “Machine learning for everyone” 

    MIT News

    From MIT News

    July 9, 2019
    Emily Makowski | School of Engineering

    1
    Mingman Zhao, a PhD student in EECS, spoke to the inaugural 6.883/6.S083 class about common issues in using machine learning tools to address problems. Photo: Lillie Paquette/School of Engineering

    A new EECS course on applications of machine learning teaches students from a variety of disciplines about one of today’s hottest topics.

    A graduate student researching red blood cell production, another studying alternative aviation fuels, and an MBA candidate: What do they have in common? They all enrolled in 6.883/6.S083 (Modeling with Machine Learning: From Algorithms to Applications) in spring 2019. The class, offered for the first time during that term, focused on machine learning applications in engineering and the sciences, attracting students from fields ranging from biology to business to architecture.

    Among them was Thalita Berpan, who was in her last term before graduating from the MIT Sloan School of Management in June. Berpan previously worked in asset management, where she observed how financial companies increasingly focus on machine learning and related technologies. “I wanted to come to business school to dive into emerging technology and get exposure to all of it,” says Berpan, who has also taken courses on blockchain and robotics. “I thought; ‘Why not take the class so I can understand the building blocks?’”

    The class provided Berpan with a thorough grounding in the basics — and more. “Not only do you understand the fundamentals of machine learning, but you actually know how to use them and apply them,” she says. “It’s very satisfying to know how to build machine learning algorithms myself and know what they mean.”

    Berpan plans to use what she has learned about data and algorithms to work with design engineers in her post-graduation job in project management. “What are some of the ways that engineers and data scientists can leverage a data set? For me to be able to help guide them through that process is going to be extremely useful,” she says.

    Open to both undergraduate and graduate students, 6.883/6.S083 enrolled 66 students for credit in its debut semester. It was created as an experimental alternative to 6.036 (Introduction to Machine Learning), a course that professors Regina Barzilay and Tommi Jaakkola developed and initially taught, and which has become one of the most popular on campus since its introduction in 2013.

    Having received feedback that 6.036 was too specialized for some non-electrical engineering and computer science (EECS) majors, Barzilay and Jaakkola designed 6.883/6.S083 to focus on different applications of machine learning. For example, Berpan, along with students from the Department of Biology and the Department of Aeronautics and Astronautics, worked on a group project that used machine learning to predict the accuracy of DNA repair in the CRISPR/Cas9 genome-editing system.

    “It doesn’t necessarily mean that the class is easier. It just has a different emphasis,” says Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science. “Our goal was to provide the students with a set of tools that would enable them to solve problems in their respective areas of specialization.”

    The class includes live lectures that focus on modeling and online materials for building a shared background in machine learning methods, including tutorials for students who have less prior exposure to the subject. “We wanted to help students learn how to model and predict, and understand when they succeeded — skills that are increasingly needed across the Institute,” says Jaakkola, the Thomas Siebel Professor in EECS and the Institute for Data, Systems, and Society (IDSS).

    In fact, about two-thirds of those enrolled for spring term were non-EECS majors. “We had a surprising number of people from the MIT School of Architecture and Planning. That’s very exciting,” Jaakkola says.

    Ultimately, the instructors say, the new course was built to bring a variety of students together to study an exciting, fast-growing area. “They constantly hear about the wonders of AI, and this enables them to become part of it,” says Barzilay. “Obviously, it brings challenges, too, because they are now in totally new, uncharted territory. But I think they are learning a lot about their abilities to expand to new areas.”

    See the full article here .


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    Please help promote STEM in your local schools.


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    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

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  • richardmitnick 12:13 pm on July 4, 2019 Permalink | Reply
    Tags: "An atomic-scale erector set", "Discretization-" whereby a building is divided into different points, , Kostas Keremidis of the MIT Concrete Sustainability Hub is modeling structures as ensembles of atoms, , MIT, Modeling a building as a collection of points that interact through forces like those found at the atomic scale,   

    From MIT News: “An atomic-scale erector set” 

    MIT News

    From MIT News

    July 3, 2019
    Andrew Logan

    1
    A building modeled with the molecular dynamics-based structural modeling approach. Image courtesy of Kostas Keremidis

    To predict building damage, Kostas Keremidis of the MIT Concrete Sustainability Hub is modeling structures as ensembles of atoms.

    To design buildings that can withstand the largest of storms, Kostas Keremidis, a PhD candidate at the MIT Concrete Sustainability Hub, is using research at the smallest scale — that of the atom.

    His approach, which derives partially from materials science, models a building as a collection of points that interact through forces like those found at the atomic scale.

    “When you look at a building, it is actually a series of connections between columns, windows, doors, and so on,” says Keremidis. “Our new framework looks at how different building components connect together to form a building like atoms form a molecule — similar forces hold them together, both at the atomic and building scale.” The framework is called molecular dynamics-based structural modeling.

    Eventually, Keremidis hopes it will provide developers and builders with a new way to readily predict building damage from disasters like hurricanes and earthquakes.

    Making models

    But before he can predict building damage, Keremidis must first assemble a model.

    He begins by taking a building and dividing its respective elements into nodes, or “atoms.” This is a standard procedure called “discretization,” whereby a building is divided into different points. Then he gives each “atom” different properties according to its material. For example, the weight of each “atom” may depend on if it’s part of a floor, a door, a window, and so on. After modeling them, he defines their bonds.

    The first type of bond between points in a building model is called an axial bond. These describe how elements deform under a load in the direction of their span — in other words, they model how a column shrinks and then rebounds under a load, like a spring.

    The second type of connection is that of the angular bonds, which represent how elements like a beam bend in the lateral direction. Keremidis uses these vertical and lateral interactions to model the deformation and breaking of different building elements. Breaking occurs when these bonds deform too much, just like in real structures.

    To see how one of his buildings will fare under conditions like storms or earthquakes, Keremidis must thoroughly test these assembled atoms and their bonds under numerous simulations.

    “Once I have my model and my building, I then run around 10,000 simulations,” explains Keremidis. “I can assign 10,000 different loads to one element or building, or I can also assign that element 10,000 different properties.”

    For him to assess the results of these simulated conditions or properties, Keremidis returns to the bonds. “When they deform during a simulation, these bonds will try to bring the building back to its original position,” he notes. “But they may also get damaged, too. This is how we model damage — we count how many bonds are destroyed and where.”

    The damage is in the details

    The model’s innovations actually lie in its damage prediction.

    Traditionally, engineers have used a method called finite element analysis to model building damage. Like MIT’s approach, it also breaks down a building into component parts. But it is generally a time-consuming technique that is set up around the elasticity of elements. This means that it can model only small deformations in a building, rather than large-scale inelastic deformations, like fracture, that frequently occur under hurricane loads.

    An added benefit of his molecular dynamics model is that Keremidis can explore “different materials, different structural properties, and different building geometries” by playing with the layout and nature of atoms and their bonds. This means that molecular dynamics can potentially model any element of a building, and more quickly, too.

    By scaling this approach beyond individual buildings, molecular dynamics could also better inform city, state, and even federal hazard-mitigation efforts.

    For hazard mitigation, cities currently rely on a model by the Federal Emergency Management Agency (FEMA) called HAZUS. It takes historical weather data and a dozen standard building models to predict the damage that a community might experience during a hazard.

    While useful, HAZUS is not ideal. It offers around only a dozen standardized building types and provides qualitative, rather than quantitative, results.

    The MIT model, however, will allow stakeholders to go into finer detail. “With FEMA’s HAZUS, the current level of categorization is too coarse. Instead, we should have 50 or 60 building types,” says Keremidis. “Our model will allow us to collect and model this wider range of buildings types.”

    Since it measures damage by counting the broken bonds between atoms, a molecular dynamics approach will also more easily quantify the damage that hazards like windstorms or earthquakes can inflict on a community. Such a quantifiable understanding of hazard damage should lead to more accurate estimations of mitigation costs and recovery.

    According to the U.S. Congressional Budget Office, wind storms currently cause $28 billion in damage annually. By 2075, they will cause $38 billion, due to climate change and coastal development.

    With a molecular dynamics approach, developers and government agencies will have one more tool to predict and mitigate these damages.

    The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation.

    Research Brief: Resilience Assessment of Structures Using Molecular Dynamics

    Research Brief: Validation of Molecular Dynamics-Based Structural Damage Models

    See the full article here .


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    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

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  • richardmitnick 11:49 am on July 3, 2019 Permalink | Reply
    Tags: , GANpaint system developed at MIT can easily add features to an existing image., MIT,   

    From MIT News: “Teaching artificial intelligence to create visuals with more common sense” 

    MIT News

    From MIT News

    July 1, 2019
    Adam Conner-Simons | MIT CSAIL

    1
    The GANpaint system developed at MIT can easily add features to an existing image. At left, the original photo of a kitchen; at right, the same kitchen with the addition of a window. Co-author Jun-Yan Zhu believes better understanding of GANs will help researchers be able to better stamp out fakery: “This understanding may potentially help us detect fake images more easily.”

    2
    GANpaint Studio general interface

    An MIT/IBM system could help artists and designers make quick tweaks to visuals while also helping researchers identify “fake” images.

    David Bau, a PhD student at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), describes the project as one of the first times computer scientists have been able to actually “paint with the neurons” of a neural network — specifically, a popular type of network called a generative adversarial network (GAN).

    Available online as an interactive demo, GANpaint Studio allows a user to upload an image of their choosing and modify multiple aspects of its appearance, from changing the size of objects to adding completely new items like trees and buildings.

    Boon for designers

    Spearheaded by MIT professor Antonio Torralba as part of the MIT-IBM Watson AI Lab he directs, the project has vast potential applications. Designers and artists could use it to make quicker tweaks to their visuals. Adapting the system to video clips would enable computer-graphics editors to quickly compose specific arrangements of objects needed for a particular shot. (Imagine, for example, if a director filmed a full scene with actors but forgot to include an object in the background that’s important to the plot.)

    GANpaint Studio could also be used to improve and debug other GANs that are being developed, by analyzing them for “artifact” units that need to be removed. In a world where opaque AI tools have made image manipulation easier than ever, it could help researchers better understand neural networks and their underlying structures.

    “Right now, machine learning systems are these black boxes that we don’t always know how to improve, kind of like those old TV sets that you have to fix by hitting them on the side,” says Bau, lead author on a related paper about the system with a team overseen by Torralba. “This research suggests that, while it might be scary to open up the TV and take a look at all the wires, there’s going to be a lot of meaningful information in there.”

    One unexpected discovery is that the system actually seems to have learned some simple rules about the relationships between objects. It somehow knows not to put something somewhere it doesn’t belong, like a window in the sky, and it also creates different visuals in different contexts. For example, if there are two different buildings in an image and the system is asked to add doors to both, it doesn’t simply add identical doors — they may ultimately look quite different from each other.

    “All drawing apps will follow user instructions, but ours might decide not to draw anything if the user commands to put an object in an impossible location,” says Torralba. “It’s a drawing tool with a strong personality, and it opens a window that allows us to understand how GANs learn to represent the visual world.”

    GANs are sets of neural networks developed to compete against each other. In this case, one network is a generator focused on creating realistic images, and the second is a discriminator whose goal is to not be fooled by the generator. Every time the discriminator ‘catches’ the generator, it has to expose the internal reasoning for the decision, which allows the generator to continuously get better.

    “It’s truly mind-blowing to see how this work enables us to directly see that GANs actually learn something that’s beginning to look a bit like common sense,” says Jaakko Lehtinen, an associate professor at Finland’s Aalto University who was not involved in the project. “I see this ability as a crucial steppingstone to having autonomous systems that can actually function in the human world, which is infinite, complex and ever-changing.”

    Stamping out unwanted “fake” images

    The team’s goal has been to give people more control over GAN networks. But they recognize that with increased power comes the potential for abuse, like using such technologies to doctor photos. Co-author Jun-Yan Zhu says that he believes that better understanding GANs — and the kinds of mistakes they make — will help researchers be able to better stamp out fakery.

    “You need to know your opponent before you can defend against it,” says Zhu, a postdoc at CSAIL. “This understanding may potentially help us detect fake images more easily.”

    To develop the system, the team first identified units inside the GAN that correlate with particular types of objects, like trees. It then tested these units individually to see if getting rid of them would cause certain objects to disappear or appear. Importantly, they also identified the units that cause visual errors (artifacts) and worked to remove them to increase the overall quality of the image.

    “Whenever GANs generate terribly unrealistic images, the cause of these mistakes has previously been a mystery,” says co-author Hendrik Strobelt, a research scientist at IBM. “We found that these mistakes are triggered by specific sets of neurons that we can silence to improve the quality of the image.”

    Bau, Strobelt, Torralba and Zhu co-wrote the paper with former CSAIL PhD student Bolei Zhou, postdoctoral associate Jonas Wulff, and undergraduate student William Peebles. They will present it next month at the SIGGRAPH conference in Los Angeles. “This system opens a door into a better understanding of GAN models, and that’s going to help us do whatever kind of research we need to do with GANs,” says Lehtinen.

    See the full article here .


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    Please help promote STEM in your local schools.


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    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

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  • richardmitnick 1:12 pm on July 1, 2019 Permalink | Reply
    Tags: "How Greentown Labs became the epicenter of clean tech", 800 jobs- many in the Boston area., Greentown Labs is the largest clean technology incubator in North America, Greentown offers startups equity-free legal information technology marketing and sales support and a coveted network of corporations and industry investors., MIT, Since its inception Greentown has supported more than 200 startups that have created around 2   

    From MIT News: “How Greentown Labs became the epicenter of clean tech” 

    MIT News

    From MIT News

    June 25, 2019
    Zach Winn

    1
    Greentown Labs is the largest clean technology incubator in North America by both square feet and the number of member companies. The open layout of its entrance, shown here, is designed to host events and encourage collaboration. Images: Barry Hetherington

    2
    “Greentown offers a lot of different things, but first and foremost among them is a community of entrepreneurs who are striving to solve big challenges in climate, energy, and the environment,” says Greentown Labs CEO Emily Reichert MBA ’12. Images: Barry Hetherington

    The incubator’s winding journey to success helped its startup community grow closer while addressing environmental challenges.

    Greentown Labs is the largest clean technology incubator in North America, a fact that’s easy to accept when you walk inside. The massive, open entrance of Greentown’s Somerville, Massachusetts, headquarters gives visitors the impression they’ve entered the office of one of Greater Boston’s most successful tech companies.

    Beyond the modern entryway are smaller working spaces — some cluttered with startup prototypes, others lined with orderly lab equipment — to enable foundational, company-building experiments.

    In addition to the space and equipment, Greentown offers startups equity-free legal, information technology, marketing, and sales support, and a coveted network of corporations and industry investors.

    But what many entrepreneurs say they like most about Greentown is the people.

    “Greentown offers a lot of different things, but first and foremost among them is a community of entrepreneurs who are striving to solve big challenges in climate, energy, and the environment,” says Greentown Labs CEO Emily Reichert MBA ’12.

    Greentown is full of stories of peers bumping into each other in the kitchen only to find they’re struggling with similar problems or, even better, that one of them already grappled with the problem and found a solution.

    MIT has played a pivotal role in Greentown’s success since its inception. Reichert estimates about 60 percent of all the companies that have come through Greentown have direct ties to MIT.

    The current version of Greentown looks like the result of some well-funded, grand vision set forth long ago. But Greentown’s rise was every bit as spontaneous — and tenuous — as the early days of any startup.

    A space for building

    In 2010, Sorin Grama SM ’07 and Sam White were looking for office space to work on a new chiller design for their startup, Promethean Power Systems, which still develops off-grid refrigeration systems in India. They needed a place to build the big, leaky refrigeration prototypes they’d thought up. It also needed to be close to MIT, where the company founders connected with advisors and interns.

    Eventually, White found “a dilapidated warehouse” on Charles Street in Cambridge for the right price. What the space lacked in beauty it made up for in size, so the founders decided to use an MIT email list to see if other founders would like to join them. Some founders building an app were first to respond. Their first reaction was to ask White and Grama to clean up a bit, and they were politely shown the door.

    Without exactly intending to, Grama and White had made their warehouse a builder space. Over the next week, a few more founders came in, including Jason Hanna, the co-founder of building efficiency company Embue; Jeremy Pitts SM ’10, MBA ’10, who was creating more efficient compressor systems for the oil and gas industry as the founder of Oscomp Systems; and Adam Rein MBA ’10 and Ben Glass ’07 SM ’10, whose company Altaeros was building airborne wind turbines. The warehouse looked perfect to them.

    “What we all had in common was we just needed a space to prototype and build stuff, where we could spill stuff, make noise, and share tools,” Grama says. “Pretty quickly it became a nice band of startups that appreciated the same thing.”

    The winter of 2010-2011 was a freezing one in the warehouse, made worse by icy cement floors, but the founders couldn’t help but notice the benefits of working together. Any time an intern or investor came to see one company, they were introduced to the others. Founders with expertise in areas like grant writing or funding rounds would give lunchtime presentations to help the others.

    Rein remembers thinking he was in the perfect environment to succeed despite the sometimes comical dysfunction of the space. One day an official with the United States Agency for International Development (USAID) stopped by to evaluate one of the startups for a grant. The visit went well enough — until she got locked in the bathroom. The founders eventually got her out, but they didn’t think the incident boded for their chances of getting that grant.

    When the landlord kicked them out of Charles Street, they found a similar space in South Boston, recruiting friends and employees to help strip wires, scrape walls, and paint over the course of a week. Rein recalls his regular duties included ordering toilet paper for the building.

    The space was also twice as large as the one in Cambridge, so as Greentown’s reputation spread throughout 2011, five startups became 15, then 20.

    “It really took on a life of its own,” Grama says.

    Among the curious MIT students who journeyed to Greentown that year was Reichert. Having worked as a chemist for 10 years in spotless, safety-certified labs before coming to MIT, she was shocked to see the condition of Greentown.

    “The first time I walked in I had two gut reactions,” Reichert says. “The first was I felt this amazing energy and passion, and kind of a buzzing. If you walk into Greentown today you still feel those things. The second was, ‘Oh my god, this place is a death trap.’”

    After earning her MBA, Reichert initially helped out as a consultant at Greentown. By February of 2013, she joined Greentown to run it full time. It was a critical time for the growing co-op: White and Grama were getting ready to move to India to work on Promethean, and Hanna, who had primarily led Greentown to that point, was expecting the birth of his first child.

    At the same time, real estate prices in South Boston were skyrocketing, and Greentown was again being forced to move.

    Reichert, who worked as CEO without a salary for more than a year, remembers those first six months on the job as the most stressful of her life. With no money to put toward a new space, she was able to partner with the City of Somerville to secure some funding and find a new location. Reichert signed a construction contract to renovate the Somerville space before she knew where the money would come from, and began lobbying state and corporate officials for sponsorships.

    She still remembers the day Greentown was to be evicted from South Boston, with everyone scrambling to clean out the cluttered warehouse and a few determined founders running one last experiment until 7 p.m. before throwing the last of the equipment in a U-Haul truck and beginning the next phase of Greentown’s journey.

    Growing up

    Within 15 months of the move to Somerville, Greentown’s 40,000 square feet were completely filled and Reichert began the process of expanding the headquarters.

    Today, Greentown’s three buildings make up more than 100,000 square feet of prototyping, office, and event space and feature a wet lab, electronics lab, and machine shop.

    Since its inception, Greentown has supported more than 200 startups that have created around 2,800 jobs, many in the Boston area.

    The original founders still serve on Greentown’s board of directors, ensuring every dollar Greentown makes goes toward supporting startups.

    Of the founding companies, only Promethean and Altaeros are still housed in Greentown, although they’re all still operating in some form.

    “We probably should’ve moved out, but it’s important to work in a place you really enjoy,” Rein says of Altaeros.

    Grama, meanwhile, has come full circle. After ceding the reigns of Promethean and returning from India, last year he started another company, Transaera, that’s developing efficient, environmentally friendly cooling systems based on research from MIT.

    This time, it took him a lot less time to find office space.

    See the full article here .


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    Please help promote STEM in your local schools.


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    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

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  • richardmitnick 10:39 am on July 1, 2019 Permalink | Reply
    Tags: "New AI programming language goes beyond deep learning", a novel probabilistic-programming system named “Gen.”, MIT   

    From MIT News: “New AI programming language goes beyond deep learning” 

    MIT News

    From MIT News

    June 26, 2019
    Rob Matheson

    1
    Users feed Gen relatively short code defining a target task, and the system automatically generates the results. Image: Chelsea Turner, MIT

    General-purpose language works for computer vision, robotics, statistics, and more.

    A team of MIT researchers is making it easier for novices to get their feet wet with artificial intelligence, while also helping experts advance the field.

    In a paper presented at the Programming Language Design and Implementation conference this week, the researchers describe a novel probabilistic-programming system named “Gen.” Users write models and algorithms from multiple fields where AI techniques are applied — such as computer vision, robotics, and statistics — without having to deal with equations or manually write high-performance code. Gen also lets expert researchers write sophisticated models and inference algorithms — used for prediction tasks — that were previously infeasible.

    In their paper, for instance, the researchers demonstrate that a short Gen program can infer 3-D body poses, a difficult computer-vision inference task that has applications in autonomous systems, human-machine interactions, and augmented reality. Behind the scenes, this program includes components that perform graphics rendering, deep-learning, and types of probability simulations. The combination of these diverse techniques leads to better accuracy and speed on this task than earlier systems developed by some of the researchers.

    Due to its simplicity — and, in some use cases, automation — the researchers say Gen can be used easily by anyone, from novices to experts. “One motivation of this work is to make automated AI more accessible to people with less expertise in computer science or math,” says first author Marco Cusumano-Towner, a PhD student in the Department of Electrical Engineering and Computer Science. “We also want to increase productivity, which means making it easier for experts to rapidly iterate and prototype their AI systems.”

    The researchers also demonstrated Gen’s ability to simplify data analytics by using another Gen program that automatically generates sophisticated statistical models typically used by experts to analyze, interpret, and predict underlying patterns in data. That builds on the researchers’ previous work that let users write a few lines of code to uncover insights into financial trends, air travel, voting patterns, and the spread of disease, among other trends. This is different from earlier systems, which required a lot of hand coding for accurate predictions.

    “Gen is the first system that’s flexible, automated, and efficient enough to cover those very different types of examples in computer vision and data science and give state of-the-art performance,” says Vikash K. Mansinghka ’05, MEng ’09, PhD ’09, a researcher in the Department of Brain and Cognitive Sciences who runs the Probabilistic Computing Project.

    Joining Cusumano-Towner and Mansinghka on the paper are Feras Saad ’15, SM ’16, and Alexander K. Lew, both CSAIL graduate students and members of the Probabilistic Computing Project.

    Best of all worlds

    In 2015, Google released TensorFlow, an open-source library of application programming interfaces (APIs) that helps beginners and experts automatically generate machine-learning systems without doing much math. Now widely used, the platform is helping democratize some aspects of AI. But, although it’s automated and efficient, it’s narrowly focused on deep-learning models which are both costly and limited compared to the broader promise of AI in general.

    But there are plenty of other AI techniques available today, such as statistical and probabilistic models, and simulation engines. Some other probabilistic programming systems are flexible enough to cover several kinds of AI techniques, but they run inefficiently.

    The researchers sought to combine the best of all worlds — automation, flexibility, and speed — into one. “If we do that, maybe we can help democratize this much broader collection of modeling and inference algorithms, like TensorFlow did for deep learning,” Mansinghka says.

    In probabilistic AI, inference algorithms perform operations on data and continuously readjust probabilities based on new data to make predictions. Doing so eventually produces a model that describes how to make predictions on new data.

    Building off concepts used in their earlier probabilistic-programming system, Church, the researchers incorporate several custom modeling languages into Julia, a general-purpose programming language that was also developed at MIT. Each modeling language is optimized for a different type of AI modeling approach, making it more all-purpose. Gen also provides high-level infrastructure for inference tasks, using diverse approaches such as optimization, variational inference, certain probabilistic methods, and deep learning. On top of that, the researchers added some tweaks to make the implementations run efficiently.

    Beyond the lab

    External users are already finding ways to leverage Gen for their AI research. For example, Intel is collaborating with MIT to use Gen for 3-D pose estimation from its depth-sense cameras used in robotics and augmented-reality systems. MIT Lincoln Laboratory is also collaborating on applications for Gen in aerial robotics for humanitarian relief and disaster response.

    Gen is beginning to be used on ambitious AI projects under the MIT Quest for Intelligence. For example, Gen is central to an MIT-IBM Watson AI Lab project, along with the U.S. Department of Defense’s Defense Advanced Research Projects Agency’s ongoing Machine Common Sense project, which aims to model human common sense at the level of an 18-month-old child. Mansinghka is one of the principal investigators on this project.

    “With Gen, for the first time, it is easy for a researcher to integrate a bunch of different AI techniques. It’s going to be interesting to see what people discover is possible now,” Mansinghka says.

    Zoubin Ghahramani, chief scientist and vice president of AI at Uber and a professor at Cambridge University, who was not involved in the research, says, “Probabilistic programming is one of most promising areas at the frontier of AI since the advent of deep learning. Gen represents a significant advance in this field and will contribute to scalable and practical implementations of AI systems based on probabilistic reasoning.”

    Peter Norvig, director of research at Google, who also was not involved in this research, praised the work as well. “[Gen] allows a problem-solver to use probabilistic programming, and thus have a more principled approach to the problem, but not be limited by the choices made by the designers of the probabilistic programming system,” he says. “General-purpose programming languages … have been successful because they … make the task easier for a programmer, but also make it possible for a programmer to create something brand new to efficiently solve a new problem. Gen does the same for probabilistic programming.”

    Gen’s source code is publicly available and is being presented at upcoming open-source developer conferences, including Strange Loop and JuliaCon. The work is supported, in part, by DARPA.

    See the full article here .


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    Please help promote STEM in your local schools.


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    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

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  • richardmitnick 12:36 pm on June 26, 2019 Permalink | Reply
    Tags: , , Catherine Drennan, , , Drennan seized on X-ray crystallography as a way to visualize molecular structures., MIT,   

    From MIT News: Women in STEM- “For Catherine Drennan, teaching and research are complementary passions” 

    MIT News

    From MIT News

    June 26, 2019
    Leda Zimmerman

    1
    “Really the most exciting thing for me is watching my students ask good questions, problem-solve, and then do something spectacular with what they’ve learned,” says Professor Catherine Drennan. Photo: James Kegley

    Professor of biology and chemistry is catalyzing new approaches in research and education to meet the climate challenge.

    Catherine Drennan says nothing in her job thrills her more than the process of discovery. But Drennan, a professor of biology and chemistry, is not referring to her landmark research on protein structures that could play a major role in reducing the world’s waste carbons.

    “Really the most exciting thing for me is watching my students ask good questions, problem-solve, and then do something spectacular with what they’ve learned,” she says.

    For Drennan, research and teaching are complementary passions, both flowing from a deep sense of “moral responsibility.” Everyone, she says, “should do something, based on their skill set, to make some kind of contribution.”

    Drennan’s own research portfolio attests to this sense of mission. Since her arrival at MIT 20 years ago, she has focused on characterizing and harnessing metal-containing enzymes that catalyze complex chemical reactions, including those that break down carbon compounds.

    She got her start in the field as a graduate student at the University of Michigan, where she became captivated by vitamin B12. This very large vitamin contains cobalt and is vital for amino acid metabolism, the proper formation of the spinal cord, and prevention of certain kinds of anemia. Bound to proteins in food, B12 is released during digestion.

    “Back then, people were suggesting how B12-dependent enzymatic reactions worked, and I wondered how they could be right if they didn’t know what B12-dependent enzymes looked like,” she recalls. “I realized I needed to figure out how B12 is bound to protein to really understand what was going on.”

    Drennan seized on X-ray crystallography as a way to visualize molecular structures. Using this technique, which involves bouncing X-ray beams off a crystallized sample of a protein of interest, she figured out how vitamin B12 is bound to a protein molecule.

    “No one had previously been successful using this method to obtain a B12-bound protein structure, which turned out to be gorgeous, with a protein fold surrounding a novel configuration of the cofactor,” says Drennan.

    Carbon-loving microbes show the way

    These studies of B12 led directly to Drennan’s one-carbon work. “Metallocofactors such as B12 are important not just medically, but in environmental processes,” she says. “Many microbes that live on carbon monoxide, carbon dioxide, or methane — eating carbon waste or transforming carbon — use metal-containing enzymes in their metabolic pathways, and it seemed like a natural extension to investigate them.”

    Some of Drennan’s earliest work in this area, dating from the early 2000s, revealed a cluster of iron, nickel, and sulfur atoms at the center of the enzyme carbon monoxide dehydrogenase (CODH). This so-called C-cluster serves hungry microbes, allowing them to “eat” carbon monoxide and carbon dioxide.

    Recent experiments by Drennan analyzing the structure of the C-cluster-containing enzyme CODH showed that in response to oxygen, it can change configurations, with sulfur, iron, and nickel atoms cartwheeling into different positions. Scientists looking for new avenues to reduce greenhouse gases took note of this discovery. CODH, suggested Drennan, might prove an effective tool for converting waste carbon dioxide into a less environmentally destructive compound, such as acetate, which might also be used for industrial purposes.

    Drennan has also been investigating the biochemical pathways by which microbes break down hydrocarbon byproducts of crude oil production, such as toluene, an environmental pollutant.

    “It’s really hard chemistry, but we’d like to put together a family of enzymes to work on all kinds of hydrocarbons, which would give us a lot of potential for cleaning up a range of oil spills,” she says.

    The threat of climate change has increasingly galvanized Drennan’s research, propelling her toward new targets. A 2017 study she co-authored in Science detailed a previously unknown enzyme pathway in ocean microbes that leads to the production of methane, a formidable greenhouse gas: “I’m worried the ocean will make a lot more methane as the world warms,” she says.

    Drennan hopes her work may soon help to reduce the planet’s greenhouse gas burden. Commercial firms have begun using the enzyme pathways that she studies, in one instance employing a proprietary microbe to capture carbon dioxide produced during steel production — before it is released into the atmosphere — and convert it into ethanol.

    “Reengineering microbes so that enzymes take not just a little, but a lot of carbon dioxide out of the environment — this is an area I’m very excited about,” says Drennan.

    Creating a meaningful life in the sciences

    At MIT, she has found an increasingly warm welcome for her efforts to address the climate challenge.

    “There’s been a shift in the past decade or so, with more students focused on research that allows us to fuel the planet without destroying it,” she says.

    In Drennan’s lab, a postdoc, Mary Andorfer, and a rising junior, Phoebe Li, are currently working to inhibit an enzyme present in an oil-consuming microbe whose unfortunate residence in refinery pipes leads to erosion and spills. “They are really excited about this research from the environmental perspective and even made a video about their microorganism,” says Drennan.

    Drennan delights in this kind of enthusiasm for science. In high school, she thought chemistry was dry and dull, with no relevance to real-world problems. It wasn’t until college that she “saw chemistry as cool.”

    The deeper she delved into the properties and processes of biological organisms, the more possibilities she found. X-ray crystallography offered a perfect platform for exploration. “Oh, what fun to tell the story about a three-dimensional structure — why it is interesting, what it does based on its form,” says Drennan.

    The elements that excite Drennan about research in structural biology — capturing stunning images, discerning connections among biological systems, and telling stories — come into play in her teaching. In 2006, she received a $1 million grant from the Howard Hughes Medical Institute (HHMI) for her educational initiatives that use inventive visual tools to engage undergraduates in chemistry and biology. She is both an HHMI investigator and an HHMI professor, recognition of her parallel accomplishments in research and teaching, as well as a 2015 MacVicar Faculty Fellow for her sustained contribution to the education of undergraduates at MIT.

    Drennan attempts to reach MIT students early. She taught introductory chemistry classes from 1999 to 2014, and in fall 2018 taught her first introductory biology class.

    “I see a lot of undergraduates majoring in computer science, and I want to convince them of the value of these disciplines,” she says. “I tell them they will need chemistry and biology fundamentals to solve important problems someday.”

    Drennan happily migrates among many disciplines, learning as she goes. It’s a lesson she hopes her students will absorb. “I want them to visualize the world of science and show what they can do,” she says. “Research takes you in different directions, and we need to bring the way we teach more in line with our research.”

    She has high expectations for her students. “They’ll go out in the world as great teachers and researchers,” Drennan says. “But it’s most important that they be good human beings, taking care of other people, asking what they can do to make the world a better place.”

    See the full article here .


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    Please help promote STEM in your local schools.


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  • richardmitnick 9:37 am on June 23, 2019 Permalink | Reply
    Tags: Grimshaw has aspirations to bring programming to Rosebud, Grimshaw hopes to eventually bring the skills and knowledge he acquires at MIT back home to the Rosebud Sioux Indian reservation., in turn teach others., Mason Grimshaw, MIT, MIT Native American student hopes to effect social change for his reservation., The ultimate dream would be to open a software or web development consulting firm where he could teach community members computer science skills that they could in turn teach others.   

    From MIT News: “A data scientist dedicated to social change” 

    MIT News

    From MIT News

    June 22, 2019
    Daysia Tolentino

    1
    Mason Grimshaw. Image: Jake Belcher

    MBAn student Mason Grimshaw seeks to bring business solutions to overlooked communities.

    Mason Grimshaw grew up on the Rosebud Sioux Indian Reservation in South Dakota but moved to Rapid City during high school to pursue a better education. When it came time to apply to college, he hopped online, typed “best engineering schools” into Google, and applied to two places: MIT and his father’s alma mater, the South Dakota School of Mines and Technology. He was admitted to both, but when he got into the Institute, his father insisted that he go.

    It wasn’t an easy decision, however. Grimshaw felt guilt about leaving his community, where he says that everyone helps each other get by. The move to Rapid City had been difficult enough for him, given that 90 percent of his family lived back at the reservation. Coming to Cambridge was an even bigger step, but his family encouraged him to take the opportunity.

    “I didn’t really want to leave home, because that is such a strong community for me. I thought if I did leave, it was only going to be worth it if I could get the best education possible,” he says.

    Now a graduate student at the MIT Sloan School of Management working toward a Master of Business Analytics (MBAn) degree, Grimshaw hopes to eventually bring the skills and knowledge he acquires at MIT back home to the reservation.

    Looking at the big picture, Grimshaw has aspirations to bring programming to Rosebud. The ultimate dream would be to open a software or web development consulting firm where he could teach community members computer science skills that they could, in turn, teach others. He hopes that through this business, he can equip people in the community with enough technical skills to be able to sustain the company on their own without his help. It’s a long-term goal, but Grimshaw aims high.

    Discovering data

    After earning his bachelor’s in business analytics at MIT, Grimshaw saw the MBAn as a natural next step. The program teaches students to apply the techniques of data science, programming, machine learning, and optimization to come up with business solutions.

    “Because I did it as an undergrad, I thought this stuff was so cool. You can kind of predict the future and help anyone make a better decision. If I was going to be that person to help people make decisions that are important and change people’s lives, I wanted to make sure that I was as prepared as possible,” Grimshaw says.

    Surprisingly, Grimshaw did not touch a line of code before coming to MIT. In fact, he entered college intending to study mechanical engineering. But in his first year, his friend was having issues with an assignment for a computer science class, so he decided to help him take a crack at the problem.

    The work was fun, Grimshaw says, and coding came naturally for him. Eventually, he dropped his mechanical engineering pursuits and started studying computer science. He later switched majors and applied his computer science education to business analytics.

    As a part of his MBAn program, he must complete an analytics capstone project, in which students work with a sponsor organization to create data-driven solutions to specific problems. Grimshaw, along with his program partner Amal Rar, will be working with the Massachusetts Bay Transportation Authority (MBTA) this summer to make The Ride, MBTA’s door-to-door paratransit service, more efficient.

    Bringing business to invisible places

    Grimshaw is also currently assisting MIT Sloan Senior Lecturer Anjali Sastry in writing a case study for South African nonprofit RLabs. RLabs seeks to inspire hope by providing business training and consulting to underprivileged South African communities. Grimshaw liked the organization’s mission, and he hopes that working on the RLabs case could give him some ideas about how to bring hope and innovation to his own community back home.

    The nonprofit has, in part, inspired some of Grimshaw’s future aspirations for Rosebud. It has also gotten him to think about alternative ways to invest in or give back to communities that don’t necessarily focus on money. Some people, he says, need a place to stay or food more immediately than they need money.

    Evaluating those circumstances and developing business models that address those more immediate needs as a form of payment can be a unique alternative to traditional compensation. Grimshaw stresses that monetary compensation is still important, but that being responsive to the specific areas of need within a community also has value.

    “There’s a fine line. You can’t just say, ‘These people have nothing so they should just be happy to have a roof over their heads.’ I’m certainly not trying to do that, but there’s a difference in values and in what people place value on. Using that to make your business a little more sustainable is interesting,” Grimshaw says.

    The reservation that Grimshaw is from lies within Todd County, an area that was previously listed as one of the poorest in America. He hopes to demonstrate to businesses that it is possible and worthwhile to invest in overlooked areas. He says that a lot of case studies in his field don’t feature stories from the emerging world or rural areas. He wants to show that through creative thinking and problem-solving, companies can work in these places, create jobs, and help lift people out of poverty.

    Family forward

    Outside of his studies, Grimshaw mostly spends time with his wife and 5-month-old son, Augustine. His face lights up as he speaks about them.

    His wife, Julia, also has a passion for helping people and works as the assistant activities director at Hale House, an assisted senior living facility in Boston. The two of them grew up together and hope to move their family closer to home after Grimshaw finishes his MBAn. For now, their favorite things to do in Boston are going to the Public Gardens (Augustine loves the grass, Grimshaw says), getting a bite at Tasty Burger in Fenway, and watching the “Great British Bake Off” at home.

    He also continues to participate in the American Indian Science and Engineering Society (AISES), which he joined as an undergraduate. There were very few members when he arrived at MIT in 2014, and while the number is still small, Grimshaw is enthusiastic about its growth.

    “It was pretty cool because when I came here there were four, and on a good day five, of us. I still go to meetings. As I go now, there’s always 10 people, sometimes up to 12 or 15, and it’s awesome to see how much it’s growing,” he says.

    While most people going into his field may opt for Silicon Valley or somewhere else on the coasts, Grimshaw would rather take his skill set closer to home. He won’t necessarily move back to Rosebud itself; somewhere within a reasonable driving-distance is more likely. He’s thinking about Denver, with its up-and-coming tech scene, but nothing is set in stone. Wherever he ends up, if a company is interested in helping others through data, Mason Grimshaw is here to help.

    See the full article here .


    five-ways-keep-your-child-safe-school-shootings
    Please help promote STEM in your local schools.


    Stem Education Coalition

    MIT Seal

    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

    MIT Campus

     
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